fix runic formatting

Author: jonas-pueschel

src/plans/conjugate_gradient_plan.jl

@@ -1062,7 +1062,7 @@ Construct the hybrid coefficient update rule.
 
 [`HybridCoefficient`](@ref), [`conjugate_gradient_descent`](@ref)
 """
-struct HybridCoefficientRule{N} <: DirectionUpdateRule where N
+struct HybridCoefficientRule{N} <: DirectionUpdateRule where {N}
     coefficients::NTuple{N, DirectionUpdateRuleStorage}
     lower_bound::DirectionUpdateRuleStorage
     lower_bound_scale::Real
@@ -1090,7 +1090,7 @@ function (u::DirectionUpdateRuleStorage{<:HybridCoefficientRule})(
     return max(u.coefficient.lower_bound_scale * β_lower_bound, min(βs...))
 end
 function show(io::IO, u::HybridCoefficientRule)
-    coefficient_str = join([repr(c.coefficient) for c = u.coefficients], ", ")
+    coefficient_str = join([repr(c.coefficient) for c in u.coefficients], ", ")
     return print(
         io,
         "Manopt.HybridCoefficientRule(coefficients=($coefficient_str)),lower_bound=$(repr(u.lower_bound.coefficient)),lower_bound_scale=$(u.lower_bound_scale))",

test/plans/test_conjugate_gradient_plan.jl

@@ -63,7 +63,7 @@ Manopt.update_rule_storage_vectors(::DummyCGCoeff) = Tuple{}
         @test cgbr.threshold == cgbr.threshold
         @test repr(LiuStoreyCoefficient(M)()) ==
             "Manopt.LiuStoreyCoefficientRule(; vector_transport_method=$pt)"
-        @test repr(HybridCoefficient(PolakRibiereCoefficient(), FletcherReevesCoefficient())(M)) == 
+        @test repr(HybridCoefficient(PolakRibiereCoefficient(), FletcherReevesCoefficient())(M)) ==
             "Manopt.HybridCoefficientRule(coefficients=(Manopt.PolakRibiereCoefficientRule(; vector_transport_method=$(pt)), Manopt.FletcherReevesCoefficientRule())),lower_bound=Manopt.SteepestDescentCoefficientRule(),lower_bound_scale=1.0)"
     end
 end

test/solvers/test_conjugate_gradient.jl

@@ -164,7 +164,7 @@ using LinearAlgebra: Diagonal, dot, eigvals, eigvecs
         s8.δ = δ2
         @test s8.coefficient(dmp, s8, 2) == dot(grad_2, diff) / dot(grad_1, grad_1)
 
-        dU = HybridCoefficient(PolakRibiereCoefficient(),FletcherReevesCoefficient())
+        dU = HybridCoefficient(PolakRibiereCoefficient(), FletcherReevesCoefficient())
         s9 = ConjugateGradientDescentState(
             M;
             p = x0,
@@ -179,8 +179,10 @@ using LinearAlgebra: Diagonal, dot, eigvals, eigvecs
         @test s9.coefficient(dmp, s9, 1) == 0.0 #max(0.0, min(0.0, 1.0))
         s9.X = grad_2
         s9.δ = δ2
-        @test s9.coefficient(dmp, s9, 2) == max(0.0, 
-            min(dot(grad_2, diff) / dot(grad_1, grad_1),dot(grad_2, grad_2) / dot(grad_1, grad_1)))
+        @test s9.coefficient(dmp, s9, 2) == max(
+            0.0,
+            min(dot(grad_2, diff) / dot(grad_1, grad_1), dot(grad_2, grad_2) / dot(grad_1, grad_1))
+        )
 
     end
     @testset "Conjugate Gradient runs – Low Rank matrix approx" begin